Quad tree based encoders do brute force search for finding out the best partition for codingunit (CU). This brute force search performs encoding for all the possible block sizes and selects the partition size that gi...
详细信息
ISBN:
(纸本)9781467356046
Quad tree based encoders do brute force search for finding out the best partition for codingunit (CU). This brute force search performs encoding for all the possible block sizes and selects the partition size that gives best compression. This search along with inherent complexity of the latest encoders makes it extremely difficult to attain real time performance of 30 fps and low power. The solution to this problem is to perform a low complexity analysis of the codingunit and suggest the partition of the CU based on the available CU characteristics without performing entire encoding to estimate the cost. The present paper describes a method to do this using Sum of Absolute Difference, hereby SAD, and gradient information of the codingunit. We show that the presented method results in 3x faster encoding when compared to the brute force algorithm with small increase in bitrate (approximately 5% increase in worst case) and no change in subjective quality. The complexity bitrate trade off and the result BD-PSNR values of this method are also presented.
Sample adaptive offset (SAO) filter algorithm is introduced by HEVC as a completely new stage to improve video quality. It is located after deblocking filter, adding an offset value to each sample based on the reconst...
详细信息
ISBN:
(纸本)9781479983537
Sample adaptive offset (SAO) filter algorithm is introduced by HEVC as a completely new stage to improve video quality. It is located after deblocking filter, adding an offset value to each sample based on the reconstructed data and the original YUV. In order to solve the problem of high computational complexity of SAO, a fast SAO algorithm based on coding unit partition is proposed in this paper. Evaluating the texture complexity of codingunit by the depth of block partitioning, and only the complex codingunit performed the SAO filter. Simulation results show that, compared with the HEVC conference software HM9.0, the proposed algorithm can reduce about 44.55% encoding time up to 83.68%, while it suffers from negligible on PSNR performance.
Sample adaptive offset（SAO） filter algorithm is introduced by HEVC as a completely new stage to improve video *** is located after deblocking filter,adding an offset value to each sample based on the reconstructed d...
详细信息
Sample adaptive offset（SAO） filter algorithm is introduced by HEVC as a completely new stage to improve video *** is located after deblocking filter,adding an offset value to each sample based on the reconstructed data and the original *** order to solve the problem of high computational complexity of SAO,a fast SAO algorithm based on coding unit partition is proposed in this *** the texture complexity of codingunit by the depth of block partitioning,and only the complex codingunit performed the SAO *** results show that,compared with the HEVC conference software HM9.0,the proposed algorithm can reduce about 44.55%encoding time up to 83.68%,while it suffers from negligible on PSNRperformance.
360-degree videos have drawn great attention from both the academia and the industry. For transmission and storage of 360-degree video, the joint video exploration team proposed the Versatile Video coding (VVC) standa...
详细信息
360-degree videos have drawn great attention from both the academia and the industry. For transmission and storage of 360-degree video, the joint video exploration team proposed the Versatile Video coding (VVC) standard. VVC can significantly reduce the bitrate while maintaining the same subjective visual quality compared to the preceding high efficiency video coding. However, the computational complexity of VVC is extremely high which hinders the interactive applications. This paper proposes a fast intra partition method and mode prediction algorithm for equirectangular projection 360-degree video coding. First, a latitude-based preprocessing is introduced to early terminate the codingunit (CU) partition in the polar region. Second, the support vector machine is used to predict the CU partition type. Third, the fast intra mode search method accelerates the intra mode prediction. Experimental results show that the proposed algorithm can significantly obtain an average time reduction rate of 60.40% and a Bjontegaard delta rate increase of 1.96%.
Screen content coding (SCC) in Versatile Video coding (VVC) improves the coding efficiency of screen content videos (SCVs) significantly but results in high computational complexity due to the quad-tree plus multi-typ...
详细信息
Screen content coding (SCC) in Versatile Video coding (VVC) improves the coding efficiency of screen content videos (SCVs) significantly but results in high computational complexity due to the quad-tree plus multi-type tree (QTMT) structure of the codingunit (CU) partitioning. Therefore, we make the first attempt to reduce the encoding complexity from the perspective of CU partitioning for SCC in VVC. To this end, a fast CU partition prediction method is technically developed for VVC-SCC. First, to solve the problem of lacking sufficient SCC training data, SCVs are collected to establish a database containing CUs of various sizes and corresponding partition labels. Second, to determine the partition decision in advance, a novel WA-CNN model is proposed, which is capable of predicting two large CUs for VVC-SCC by adjusting the feature channels based on the size of input CU blocks. Finally, considering the imbalanced proportion of diverse partition decisions, a loss function with the weight that equalizes the contribution of imbalanced data is formulated to train the proposed WA-CNN model. Experimental results show that the proposed model reduces the SCC intra-encoding time by 35.65%similar to 38.31% with an average of 1.84%similar to 2.42% BDBR increase.
Aiming at accelerating the intra coding process of versatile video coding (VVC), previous efforts are made to predict the quad-tree plus multi-type tree (QTMT) partition structure, in which the prediction is modeled a...
详细信息
Aiming at accelerating the intra coding process of versatile video coding (VVC), previous efforts are made to predict the quad-tree plus multi-type tree (QTMT) partition structure, in which the prediction is modeled as a classification procedure. The existing deep learning-based methods usually represent the block partition as a structural output and utilize a single convolutional neural network (CNN) for the prediction of all blocks. However, they take different blocks equally, i.e., one-for-all, which ignores that the blocks with different complexity of partition structures are unevenly distributed and have different prediction difficulties. To address this problem, we propose a novel block-dependent partition decision (BDPD) framework to adaptively process different blocks by networks with different capacities. Specifically, we design a partition homogeneity map (PHM) to represent the QTMT-based block partition, which combines different partition directions to effectively reflect the complexity of the partition structure. On this basis, we propose a class-based prediction to distinguish blocks with different complexity of the partition structure and adopt appropriate FCN models to predict PHM, incorporating block classification and PHM prediction. The blocks are classified into different classes according to their coarse texture and neighboring PHMs. Then different fully convolutional network (FCN) models are utilized to predict PHM for different classes. The FCN models with different capacities are trained in the corresponding class, respectively, which achieves higher performance with less computation on the extremely unbalanced natural video. Finally, an adaptive partition decision based on predicted PHMs is adopted to conduct partition decisions for a better trade-off between rate-distortion performance and encoding complexity. Experimental results show that our approach achieves 45.7%similar to 74.5% encoding complexity reduction with 0.78%similar to 3.38% B
Versatile Video coding (VVC) significantly improves the coding efficiency over the preceding high efficiency video coding (HEVC) standard, but at the expense of much higher computational complexity. Specifically for i...
详细信息
ISBN:
(纸本)9781665449083
Versatile Video coding (VVC) significantly improves the coding efficiency over the preceding high efficiency video coding (HEVC) standard, but at the expense of much higher computational complexity. Specifically for intra coding of VVC, the computational burden is mainly on the brute-force recursive rate-distortion optimization (RDO) search of quadtree with nested multi-type tree (QTMT) based codingunit (CU) partition structure. Consequently, we propose a random forest based algorithm to reduce the complexity of CU partition. The CUs are first divided into three categories, namely simple, fuzzy, and complex CUs. For simple and complex CUs, one random forest classifier is trained to directly predict the optimal partition mode. For fuzzy CUs, another random forest is trained to predict whether the partition process is terminated or not. The experimental results show that the complexity reduction of the proposed algorithm is up to 69% as compared to the VVC reference software (VTM 7.0), and averagely 57% encoding time saving is achieved with 1.21% BDBR increase.
Since the publication of the High Efficiency Video coding standard as the newest video coding standard, several extensions have been made. Among these, the use of the screen content coding in many fields is one of the...
详细信息
ISBN:
(纸本)9781479961399
Since the publication of the High Efficiency Video coding standard as the newest video coding standard, several extensions have been made. Among these, the use of the screen content coding in many fields is one of the important extensions. In terms of coding tree unit (CTU) partitioning, rate distortion optimization is still used in screen content coding. The complexity of the process has resulted in problems in relation to real-time application. Thus, this paper proposes a fast-deciding CTU partition mode algorithm based on entropy and coding bits. Experimental results show that the proposed algorithm can save 32% of encoding time on average compared with the default algorithm in HM-12.1+RExt-5.1 with only 0.8% bit rate increment in coding performance.
High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree codingunit (CU) partition structure to improve video compression efficiency. The determinat...
详细信息
High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree codingunit (CU) partition structure to improve video compression efficiency. The determination of the optimal CU partition is achieved through the brute-force search rate-distortion optimization method, which may result in high encoding complexity and hardware implementation challenges. To address this problem, this paper proposes a method that combines convolutional neural networks (CNN) with joint texture recognition to reduce encoding complexity. First, a classification decision method based on the global and local texture features of the CU is proposed, efficiently dividing the CU into smooth and complex texture regions. Second, for the CUs in smooth texture regions, the partition is determined by terminating early. For the CUs in complex texture regions, a proposed CNN is used for predictive partitioning, thus avoiding the traditional recursive approach. Finally, combined with texture classification, the proposed CNN achieves a good balance between the coding complexity and the coding performance. The experimental results demonstrate that the proposed algorithm reduces computational complexity by 61.23%, while only increasing BD-BR by 1.86% and decreasing BD-PSNR by just 0.09 dB.
Versatile Video coding (VVC), as the latest standard, significantly improves the coding efficiency over its predecessor standard High Efficiency Video coding (HEVC), but at the expense of sharply increased complexity....
详细信息
Versatile Video coding (VVC), as the latest standard, significantly improves the coding efficiency over its predecessor standard High Efficiency Video coding (HEVC), but at the expense of sharply increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of the codingunit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT search, this paper proposes a deep learning approach to predict the QTMT-based CU partition, for drastically accelerating the encoding process of intra-mode VVC. First, we establish a large-scale database containing sufficient CU partition patterns with diverse video content, which can facilitate the data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN (MSE-CNN) model with an early-exit mechanism to determine the CU partition, in accord with the flexible QTMT structure at multiple stages. Then, we design an adaptive loss function for training the MSE-CNN model, synthesizing both the uncertain number of split modes and the target on minimized RD cost. Finally, a multi-threshold decision scheme is developed, achieving a desirable trade-off between complexity and RD performance. The experimental results demonstrate that our approach can reduce the encoding time of VVC by 44.65%similar to 66.88% with a negligible Bjontegaard delta bit-rate (BD-BR) of 1.322%similar to 3.188%, significantly outperforming other state-of-the-art approaches.
暂无评论